A Big Data Framework for Decision Making in Supply Chain

A Big Data Framework for Decision Making in Supply Chain

Kamalendu Pal (City, University of London, UK)
DOI: 10.4018/978-1-5225-5424-0.ch001

Abstract

The advent of information and communication technologies (ICT) ushers a cost-effective prospect to take care of large volumes of complex data, commonly known as “big data” in the supply chain operational environment. Big data is being generated today by web applications, social media, intelligent machines, sensors, mobile phones, and other smart handheld devices. Big data is characterized in terms of the velocity, volume, and variety with which it produces along the supply chain. This is due to recent advances in telecommunication networks along with centralized and decentralized data storage systems, which are processed thanks to modern digital computational capabilities. There is a growing interest in the use of this large volume of data and advanced analytics for diverse types of business problems in supply chain management (SCM). Such decision-support software applications employ pure mathematical techniques, artificial intelligence techniques, and sometimes uses both techniques to perform analytical operations that undercover relationships and patterns within supply chain generated big data. This chapter proposes a framework for the utilization of big data in SCM decision making. The framework is based on the SCOR (supply chain operations reference) model, which is endorsed by Supply Chain Council (SCC). The proposed framework is influenced by the enterprise potential of augmented reality and virtual reality in supply chain applications, and it identifies key categories of big data analytics applications for the key businesses of SCOR model. Finally, the chapter highlights research issues to extract insight from big data sources for enterprise decision making.
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Introduction

In today’s global economy, retail enterprises are increasingly using distributed information systems to carry out day-to-day business operations. Such systems should result in the seamless integration of retail business applications and exchange of information between applications within and across enterprise boundaries. The extraordinary growth of information and communication technologies (ICT) driven by technology companies, computer hardware and software systems has empowered all aspects of computing applications across retail enterprises. At the same time the business environment is becoming more and more complex with functional units needing increasingly inter-functional data flow for decision-making, timely and efficient procurement of manufacturing parts, management of inventory, corporate accounting, human resources, and distribution of goods and services. In this circumstance, the retail business management team requires effective information systems to enhance competitiveness by cost reduction and improved logistics. It is universally recognized by large and small-to-medium-size retail enterprises (SME) that the ability of providing the right information at the right time brings huge rewards to retail supply chain management practices.

In a typical retail supply chain, raw materials are purchased from suppliers and products are manufactured at one or more production plants. Then they are transported to intermediate storage facilities (e.g. warehouses, distribution centers) for packing and shipping to retailers or customers. The path from supplier to customer can include a few intermediaries such as wholesalers, warehouses, and retailers, depending on the products and markets. In this way, SCM relates to business activities such as inbound and outbound transportation, warehousing, and inventory control. Importantly, it also embodies the information systems necessary to monitor these business activities. Figure 1 shows a simple diagrammatic representation of a retail supply chain, which highlights some of the primary business activities.

Figure 1.

Diagrammatic representation of a retail supply chain

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Increased internationalization of retail enterprises is changing the operational practices of their supply chains, and many retailers have adopted new models, either by outsourcing or by establishing business-alliances in other countries. Globalization has also led to changes in operational practices, where retail products are manufactured in one part of the world and sold in another. The retail supply chain has become more global in its geographical scope; the international market is getting more competitive and customer demand oriented. Customers are looking for more variety as well as better quality assured products and services.

To provide better quality customer service, at no additional cost or workload, all business processes along the supply chain must be balanced. This requires trade-offs throughout the supply chain. It is necessary to think in terms of a single interconnected chain rather than narrow functional business processes when considering effective trade-offs. Seamless integration along the supply chain is challenged when there is a conflict between a retailer’s operational activities and day-to-day business plans. Therefore, the retail enterprises have realized the advantages of the objectives defined by their business plan through operational metrics-based control practices. Simultaneously, retail enterprises are evolving into new types of business on knowledge and networks in response to an environment characterized by indistinct corporate boundaries and fast-paced changes. These new breeds of retail businesses need dynamic operational performance monitoring mechanisms, which help to find acceptable solutions at the time of need.

Key Terms in this Chapter

Decision Making Systems: A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities, typically resulting in ranking, sorting, or choosing from among alternatives. DSSs serve the management, operations, and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance (i.e., unstructured and semi-structured decision problems). Decision support systems can be either fully computerized, human-powered or a combination of both.

Virtual Reality: It is a term used for computer generated three-dimensional (3D) environments that allow the user to enter and interact with synthetic environments. The users can immerse themselves to varying degrees in the computers artificial world which may either be a simulation of some form of reality or the simulation of a complex phenomenon.

Big Data Analytics: Analytics is the discovery, interpretation, and visualization of meaningful patterns in big data. In order to do this, analytics use data classification and clustering mechanisms.

Supply Chain Management: A supply chain consists of a network of key business processes and facilities, involving end users and suppliers that provide products, services and information. In this chain management, improving the efficiency of the overall chain is an influential factor; and it needs at least four important strategic issues to be considered: supply chain network design, capacity planning, risk assessment and management, and performances monitoring and measurement. Moreover, the details break down of these issues need to consider in the level of individual business processes and sub-processes; and the combined performance of this chain. The coordination of these huge business processes and their performance are of immense importance.

Augmented Reality: It is a modern technology that involved the overlay of computer graphics on the real-world application.

Radio Frequency Identification (RFID): This is a wireless technology used to identify tagged objects in certain vicinities. Generally, it has three main components: a tag, a reader and a back-end. A tag uses the open air to transmit data via radio frequency (RF) signal. It is also weak in computational capability. RFID automates information collection regarding an individual object’s location and actions.

Neural Network: Neural network is an information processing paradigm that is inspired by the way biological nervous systems, such as brain, process information. It uses a classification mechanism that is modelled after the brain and operates by modifying the input through use of weights to determine what it should output.

Internet of Things: The internet of things (IoT) is the inter-networking of physical devices, vehicles (also referred to as “connected devices” and “smart devices”), buildings, and other items; embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data.

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